There may be some advantage in using \(\delta ^*\)  as a summative measure of misspecification. If it is very small, or very large, we use Bayes or Cut respectively. However, for intermediate values of \(k\)  in Figure REF  we see that \(\delta \) -SMI does slightly better than Bayes or Cut. For this range of \(k\) , the Bayes and Cut distributions are far apart, but the misspecification is not so bad that we gain by simply cutting feedback altogether. [1]} give an example for \(\eta \) -SMI in which more dramatic gains are seen from using intermediate values.
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